中国企业情景下品牌依恋形成机理研究——基于消费者与品牌个性匹配的视角

中国企业营销与品牌管理实践中,构建品牌依恋是企业塑造忠诚客户的重要使命,其关键在于使企业的品牌人格与消费者个性相匹配。中国企业品牌化情景与品牌管理实践的实证研究发现:形成品牌依恋的消费者个性与品牌人格匹配度是非常复杂的,受到消费者参与、消费者个体差异及消费者个性匹配度(现实个性匹配度与理想个性匹配度)类型的影响。统计检验表明:现实个性匹配度对品牌依恋的影响效应显著强于理想个性匹配度,而且消费者参与、自尊感和消费者形象会强化现实个性匹配度对品牌依恋的正向效应,同时弱化消费者理想个性匹配度对品牌依恋的影响,从而具有调节作用。研究发现:中国企业应该通过对消费者个性的研究与挖掘来建立消费者与品牌之间的品牌依恋,并且应该注重品牌的人格培育及其与消费者现实个性的匹配。     

多阶模型在地区消费差异研究中的应用

 当组间差异显著时，应该运用多阶模型纳入更高阶的场景变量进行分析。对2007年广东省11市7县、区的1600个家庭的消费数据研究表明，不同城市之间居民的消费水平、边际消费倾向都存在显著差异。文章引入微观层面的家庭人均可支配收入（PDI）和是否拥有住房（HOUSE）虚拟变量来解释微观层面的家庭消费差异；引入宏观层面的地区人均可支配收入（GPDI）和是否为发达地区（D）虚拟变量作为场景变量解释家庭消费跨地区的差异。实证结果表明，微观层面变量对家庭消费差异（组内差异）的解释程度达48％以上，两个场景变量对城镇居民消费跨地区差异（组间差异）的解释程度达99％以上。     

中国城市居民婴幼儿奶粉品牌选购行为研究——以北京市为例

以北京市城市消费者为调查对象,运用统计描述法和Logit回归模型,分析消费者对不同品牌婴幼儿奶粉的选购行为及其影响因素。研究结果表明:与国内婴幼儿奶粉品牌相比,国外品牌占据明显优势,82.7%的消费者购买国外品牌;从消费者自身的认知出发,质量安全是其购买国外品牌的主要原因。Logit模型回归结果表明,风险感知、收入、家中小孩数和性别是影响消费者品牌选购的主要影响因素。     

Identification of target clusters by using the restricted normal mixture model

This paper addresses the problem of identifying groups that satisfy the specific conditions for the means of feature variables. In this study, we refer to the identified groups as “target clusters” (TCs). To identify TCs, we propose a method based on the normal mixture model (NMM) restricted by a linear combination of means. We provide an expectation–maximization (EM) algorithm to fit the restricted NMM by using the maximum-likelihood method. The convergence property of the EM algorithm and a reasonable set of initial estimates are presented. We demonstrate the method's usefulness and validity through a simulation study and two well-known data sets. The proposed method provides several types of useful clusters, which would be difficult to achieve with conventional clustering or exploratory data analysis methods based on the ordinary NMM. A simple comparison with another target clustering approach shows that the proposed method is promising in the identification.     

Application of skew-normal distribution for detecting differential expression to microRNA data

Traditional statistical modeling of continuous outcome variables relies heavily on the assumption of a normal distribution. However, in some applications, such as analysis of microRNA (miRNA) data, normality may not hold. Skewed distributions play an important role in such studies and might lead to robust results in the presence of extreme outliers. We apply a skew-normal (SN) distribution, which is indexed by three parameters (location, scale and shape), in the context of miRNA studies. We developed a test statistic for comparing means of two conditions replacing the normal assumption with SN distribution. We compared the performance of the statistic with other Wald-type statistics through simulations. Two real miRNA datasets are analyzed to illustrate the methods. Our simulation findings showed that the use of a SN distribution can result in improved identification of differentially expressed miRNAs, especially with markedly skewed data and when the two groups have different variances. It also appeared that the statistic with SN assumption performs comparably with other Wald-type statistics irrespective of the sample size or distribution. Moreover, the real dataset analyses suggest that the statistic with SN assumption can be used effectively for identification of important miRNAs. Overall, the statistic with SN distribution is useful when data are asymmetric and when the samples have different variances for the two groups.     

Robust linear clustering

Summary.  Non-hierarchical clustering methods are frequently based on the idea of forming groups around 'objects'. The main exponent of this class of methods is the k -means method, where these objects are points. However, clusters in a data set may often be due to certain relationships between the measured variables. For instance, we can find linear structures such as straight lines and planes, around which the observations are grouped in a natural way. These structures are not well represented by points. We present a method that searches for linear groups in the presence of outliers. The method is based on the idea of impartial trimming. We search for the 'best' subsample containing a proportion 1− α of the data and the best k affine subspaces fitting to those non-discarded observations by measuring discrepancies through orthogonal distances. The population version of the sample problem is also considered. We prove the existence of solutions for the sample and population problems together with their consistency. A feasible algorithm for solving the sample problem is described as well. Finally, some examples showing how the method proposed works in practice are provided.     

Selecting the precision parameter prior in Dirichlet process mixture models

We consider Dirichlet process mixture models in which the observed clusters in any particular dataset are not viewed as belonging to a finite set of possible clusters but rather as representatives of a latent structure in which objects belong to one of a potentially infinite number of clusters. As more information is revealed the number of inferred clusters is allowed to grow. The precision parameter of the Dirichlet process is a crucial parameter that controls the number of clusters. We develop a framework for the specification of the hyperparameters associated with the prior for the precision parameter that can be used both in the presence or absence of subjective prior information about the level of clustering. Our approach is illustrated in an analysis of clustering brands at the magazine Which?. The results are compared with the approach of Dorazio (2009) via a simulation study.     

Clustering of Variables Based on Watson Distribution on Hypersphere: A Comparison of Algorithms

We consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.

Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.     

Clustering objects on subsets of attributes (with discussion)

Summary.  A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.     

消费信贷对中国居民消费影响效应的实证研究

基于2007年1月至2012年10月的样本数据,采用协整检验和脉冲响应分析,从长期和短期两方面实证研究了消费信贷对中国居民消费的影响效应.研究表明,长期内消费信贷不会对居民消费产生显著影响,即增加消费信贷并不会改变人们的边际消费倾向;短期内消费信贷不仅刺激了消费需求,而且扩大消费信贷会对国内需求产生显著的正向影响.     

Outlier-free merging of homogeneous groups of pre-classified observations under contamination

We study the problem of merging homogeneous groups of pre-classified observations from a robust perspective motivated by the anti-fraud analysis of international trade data. This problem may be seen as a clustering task which exploits preliminary information on the potential clusters, available in the form of group-wise linear regressions. Robustness is then needed because of the sensitivity of likelihood-based regression methods to deviations from the postulated model. Through simulations run under different contamination scenarios, we assess the impact of outliers both on group-wise regression fitting and on the quality of the final clusters. We also compare alternative robust methods that can be adopted to detect the outliers and thus to clean the data. One major conclusion of our study is that the use of robust procedures for preliminary outlier detection is generally recommended, except perhaps when contamination is weak and the identification of cluster labels is more important than the estimation of group-specific population parameters. We also apply the methodology to find homogeneous groups of transactions in one empirical example that illustrates our motivating anti-fraud framework.     

Split Questionnaire Designs: collecting only the data that you need through MCAR and MAR designs

We call a sample design that allows for different patterns, or sets, of data items to be collected from different sample units a Split Questionnaire Design (SQD). SQDs can be thought of as incorporating missing data into survey design. This paper examines the situation where data that are not collected by an SQD can be treated as Missing Completely At Random or Missing At Random, targets are regression coefficients in a generalised linear model fitted to binary variables, and targets are estimated using Maximum Likelihood. A key finding is that it can be easy to measure the relative contribution of a respondent to the accuracy of estimated model parameters before collecting all the respondent's model covariates. We show empirically and theoretically that we could achieve a significant reduction in respondent burden with a negligible impact on the accuracy of estimates by not collecting model covariates from respondents who we identify as contributing little to the accuracy of estimates. We discuss the general implications for SQDs.     

Designing an economically optimal repetitive group-sampling plan based on loss functions

Acceptance sampling is a quality assurance tool, which provides a rule for the producer and the consumer to make acceptance or rejection decision about a lot. This paper attempts to develop a more efficient sampling plan, variables repetitive group sampling plan, based on the total loss to the producer and consumer. To design this model, two constraints are considered to satisfy the opposing priorities and requirements of the producer and the consumer by using Acceptable quality level (AQL) and Limiting quality level (LQL) points on operating characteristic (OC) curve. The objective function of this model is constructed based on the total expected loss. In order to illustrate the application of the proposed model, an example is presented. In addition, the effects of process parameters on the optimal solution and the total expected loss are studied by performing a sensitivity analysis. Finally, the efficiency of the proposed model is compared with the variables single sampling plan, the variables double sampling plan and the repetitive group sampling plan of Balamurali and Jun (2006) in terms of average sample number, total expected loss and its difference with ideal OC curve.     

Identification of the Direction of a Causal Effect by Instrumental Variables

This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is not necessary to have any continuous variables, none of the variables need to have large support, and the instrument can be binary even if the corresponding endogenous treatment variable and/or outcome is continuous. The outcome can be mismeasured or interval-measured, and the endogenous treatment variable need not even be observed. The identification results are constructive, and can be empirically implemented using standard estimation results.     

Identifying Variables Contributing to Outliers in Phase I

When a process is monitored with a T ² control chart in a Phase II setting, the MYT decomposition is a valuable diagnostic tool for interpreting signals in terms of the process variables. The decomposition splits a signaling T ² statistic into independent components that can be associated with either individual variables or groups of variables. Since these components are T ² statistics with known distributions, they can be used to determine which of the process variable(s) contribute to the signal. However, this procedure cannot be applied directly to Phase I since the distributions of the individual components are unknown. In this article, we develop the MYT decomposition procedure for a Phase I operation, when monitoring a random sample of individual observations and identifying outliers. We use a relationship between the T ² statistic in Phase I with the corresponding T ² statistic resulting when an observation is omitted from this sample to derive the distributions of these components and demonstrate the Phase I application of the MYT decomposition.     

Moments of sample moments

The problem offinding expressions for sampling moments of sample moments has been ahistorically old one. This problem is treated here, with the use of partitions and multi partitions , for the univariate as well as the multivariate case. The systematic combinatorial approach minimizes the chance of omitting any

contributions and making errors in their computation. Componentwise identification is made possible , soerrors can be located. From the complete set of general moment formulae, s pecial cases may be obtained by identifying identical variables.     

A permutation procedure for testing the equality of pattern hypotheses across groups involving correlation or covariance matrices

This paper describes a permutation procedure to test for the equality of selected elements of a covariance or correlation matrix across groups. It involves either centring or standardising each variable within each group before randomly permuting observations between groups. Since the assumption of exchangeability of observations between groups does not strictly hold following such transformations, Monte Carlo simulations were used to compare expected and empirical rejection levels as a function of group size, the number of groups and distribution type (Normal, mixtures of Normals and Gamma with various values of the shape parameter). The Monte Carlo study showed that the estimated probability levels are close to those that would be obtained with an exact test except at very small sample sizes (5 or 10 observations per group). The test appears robust against non-normal data, different numbers of groups or variables per group and unequal sample sizes per group. Power was increased with increasing sample size, effect size and the number of elements in the matrix and power was decreased with increasingly unequal numbers of observations per group.     

Choice Behavior Under Time-Variant Quality

In past studies of consumer loyalty changes in brand attributes over time were generally unobservable and treated as additional model parameters. In this study we consider ski resorts, for which observable quality attributes change frequently. Using a repeated-purchase model with observed time-variant brand attributes, indicators for state dependence, and individual heterogeneity, we show that purchase history and time-variant site characteristics have a significant and offsetting effect on repurchase decisions. This suggests a third category of consumer along with habit formers and variety seekers, the “play-it-by-ear” type, who, unaffected by purchase history, moves across brands in pursuit of high quality.     

Path analysis and determining the distribution of indirect effects via simulation

The difference between a path analysis and the other multivariate analyses is that the path analysis has the ability to compute the indirect effects apart from the direct effects. The aim of this study is to investigate the distribution of indirect effects that is one of the components of path analysis via generated data. To realize this, a simulation study has been conducted with four different sample sizes, three different numbers of explanatory variables and with three different correlation matrices. A replication of 1000 has been applied for every single combination. According to the results obtained, it is found that irrespective of the sample size path coefficients tend to be stable. Moreover, path coefficients are not affected by correlation types either. Since the replication number is 1000, which is fairly large, the indirect effects from the path models have been treated as normal and their confidence intervals have been presented as well. It is also found that the path analysis should not be used with three explanatory variables. We think that this study would help scientists who are working in both natural and social sciences to determine sample size and different number of variables in the path analysis.